scholarly journals Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

2021 ◽  
Vol 14 (4) ◽  
pp. 140-147 ◽  
Author(s):  
Danh-tuyen Vu ◽  
Tien-thanh Nguyen ◽  
Anh-huy Hoang
Keyword(s):  
Spatial Clustering ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Moran's I ◽  
Auto Correlation ◽  
Two Phases ◽  
Novel Coronavirus ◽  
Almost All ◽  
Spatial Outliers ◽  
Fourth Wave

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

scholarly journals Spatial distribution and predictors of domestic violence against women: evidence from analysis of Ethiopian demographic health survey 2016

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elias Seid ◽  
Tesfahun Melese ◽  
Kassahun Alemu
Keyword(s):  
Domestic Violence ◽  
Spatial Distribution ◽  
Regression Analysis ◽  
Violence Against Women ◽  
Health Survey ◽  
Spatial Clustering ◽  
Wife Beating ◽  
Moran’S I ◽  
Demographic Health Survey ◽  
Moran's I

Abstract Background Violence against women particularly that is committed by an intimate partner is becoming a social and public health problem across the world. Studies show that the spatial variation in the distribution of domestic violence was commonly attributed to neighborhood-level predictors. Despite the prominent benefits of spatial techniques, research findings are limited. Therefore, the current study intends to determine the spatial distribution and predictors of domestic violence among women aged 15–49 in Ethiopia. Methods Data from the Ethiopian demographic health survey 2016 were used to determine the spatial distribution of domestic violence in Ethiopia. Spatial auto-correlation statistics (both Global and Local Moran’s I) were used to assess the spatial distribution of domestic violence cases in Ethiopia. Spatial locations of significant clusters were identified by using Kuldorff’s Sat Scan version 9.4 software. Finally, binary logistic regression and a generalized linear mixed model were fitted to identify predictors of domestic violence. Result The study found that spatial clustering of domestic violence cases in Ethiopia with Moran’s I value of 0.26, Z score of 8.26, and P value < 0.01. The Sat Scan analysis identifies the primary most likely cluster in Oromia, SNNP regions, and secondary cluster in the Amhara region. The output from regression analysis identifies low economic status, partner alcohol use, witnessing family violence, marital controlling behaviors, and community acceptance of wife-beating as significant predictors of domestic violence. Conclusion There is spatial clustering of IPV cases in Ethiopia. The output from regression analysis shows that individual, relationship, and community-level predictors were strongly associated with IPV. Based upon our findings, we give the following recommendation: The government should give prior concern for controlling factors such as high alcohol consumption, improper parenting, and community norm that encourage IPV that were responsible for IPV in the identified hot spot areas.

scholarly journals Factors Affecting Spatial Variation in Vegetation Carbon Density in Pinus massoniana Lamb. Forest in Subtropical China

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 880 ◽  
Author(s):  
Pan ◽  
Sun ◽  
Ouyang ◽  
Zang ◽  
Rao ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Forest Management ◽  
Spatial Variation ◽  
Pinus Massoniana ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Carbon Density ◽  
Moran's I ◽  
Pinus Massoniana Lamb ◽  
Vegetation Carbon

Carbon density is an important indicator of carbon sequestration capacity in forest ecosystems. We investigated the vegetation carbon density of Pinus massoniana Lamb. forest in the Jiangxi Province. Based on plots investigation and measurement of the carbon content of the samples, the influencing factors and spatial variation of vegetation carbon density (including the tree layer, understory vegetation layer and litter layer) were analysed. The results showed that the average vegetation carbon density value of P. massoniana forest was 52 Mg·ha−1. The vegetation carbon density was significantly (p < 0.01) and positively correlated with the stand age, mean annual precipitation, elevation and stand density and negatively correlated with the slope and mean annual temperature. Forest management had a significant impact on vegetation carbon density. To manage P. massoniana forest for carbon sequestration as the primary objective, near-natural forest management theory should be followed, e.g., replanting broadleaf trees. These measures would promote positive succession and improve the vegetation carbon sequestration capacity of forests. The results from the global Moran’s I showed that the vegetation carbon density of P. massoniana forest had significant positive spatial autocorrelation. The results of local Moran’s I showed that the high-high spatial clusters were mainly distributed in the southern, western and eastern parts of the province. The low-low spatial clusters were distributed in the Yushan Mountains and in the northern part of the province. The fitting results of the semivariogram models showed that the spherical model was the best fitting model for vegetation carbon density. The ratio of nugget to sill was 0.45, indicating a moderate spatial correlation of carbon density. The vegetation carbon density based on kriging spatial interpolation was mainly concentrated in the range of 32.5–69.8 Mg·ha−1. The spatial distribution of vegetation carbon density regularity was generally low in the middle region and high in the peripheral region, which was consistent with the terrain characteristics of the study area.

scholarly journals Hotspots of female genital mutilation/cutting and associated factors among girls in Ethiopia: a spatial and multilevel analysis

2020 ◽  
Author(s):  
Tesfahun Taddege Geremew ◽  
Muluken Azage ◽  
Endalkachew Worku
Keyword(s):  
Female Genital Mutilation ◽  
Associated Factors ◽  
Spatial Clustering ◽  
Household Wealth ◽  
Community Level ◽  
Moran’S I ◽  
Moran's I ◽  
Factors Affecting ◽  
Genital Mutilation ◽  
Female Genital

Abstract Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice that violates the human rights of girls and women. It is widely practiced mainly in Africa including Ethiopia. There are a number of studies on the prevalence of FGM/C in Ethiopia. However, little has been devoted to its spatial epidemiology and associated factors. Hence, this study aimed to explore the spatial pattern and factors affecting FGM/C among girls in Ethiopia.Methods: A further analysis of the 2016 Ethiopia Demographic and Health Survey data was conducted, and a total of 6,985 girls nested in 603 enumeration areas were included. Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Kulldorff’s spatial scan statistics were used to detect spatial clusters of FGM/C. Multilevel logistic regression models were fitted to identify individual and community level factors affecting FGM/C.Results: Spatial clustering of FGM/C was observed (Moran’s I=0.31, p-value < 0.01), and eight significant clusters of FGM/C were detected. The most likely primary cluster was detected in the neighborhood areas of Amhara, Afar, Tigray and Oromia regions (LLR = 279.0, p< 0.01), the secondary cluster in Tigray region (LLR=67.3, p<0.01), and the third cluster in Somali region (LLR=55.5, P<0.01). In About 83% variation in the odds of FGM/C was attributed to both individual and community level factors. At individual level, older maternal age, higher number of living children, maternal circumcision, perceived believes as FGM/C is required by religion, and supporting the continuation of FGM/C practice were factors to increase the odds of FGM/C, whereas, secondary/higher maternal education, better household wealth, and media exposure were factors decreasing the odds of FGM/C. Place of residency, Region and Ethnicity were the community level factors associated with FGM/C.Conclusions: Spatial clustering of FGM/C among girls was observed, and FGM/C hotspots were detected in Afar, Amhara, Tigray, BenishangulGumuz, Oromia, SNNPR and Somali regions including Dire Dawa Town. Both individual and community level factors play a significant role in the practice of FGM/C. Hence, FGM/C hotspots require priority interventions, and it is also better to consider both individual and community level factors.

The application of Local Moran's I to identify spatial clusters and hot spots of Pb, Mo and Ti in urban soils of Yerevan

2019 ◽  
Vol 104 ◽  
pp. 116-123 ◽  
Author(s):  
Gevorg Tepanosyan ◽  
Lilit Sahakyan ◽  
Chaosheng Zhang ◽  
Armen Saghatelyan
Keyword(s):  
Hot Spots ◽  
Urban Soils ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Moran's I

scholarly journals Spatial Analysis of Beaked Whale Foraging During Two 12 kHz Multibeam Echosounder Surveys

2021 ◽  
Vol 8 ◽  
Author(s):  
Hilary Kates Varghese ◽  
Kim Lowell ◽  
Jennifer Miksis-Olds ◽  
Nancy DiMarzio ◽  
David Moretti ◽  
...  
Keyword(s):  
Marine Mammals ◽  
Spatial Clustering ◽  
Hot Spot ◽  
Moran’S I ◽  
Moran's I ◽  
Foraging Effort ◽  
Multibeam Echosounder ◽  
Spot Area ◽  
Comparison Results ◽  
Beaked Whales

To add to the growing information about the effect of multibeam echosounder (MBES) operation on marine mammals, a study was conducted to assess the spatial foraging effort of Cuvier’s beaked whales during two MBES surveys conducted in January of 2017 and 2019 off of San Clemente Island, California. The MBES surveys took place on the Southern California Antisubmarine Warfare Range (SOAR), which contains an array of 89 hydrophones covering an area of approximately 1800 km2 over which foraging beaked whales were detected. A spatial autocorrelation analysis of foraging effort was conducted using the Moran’s I (global) and the Getis-Ord Gi∗ (local) statistics, to understand the animals’ spatial use of the entire SOAR, as well as smaller areas, respectively, within the SOAR Before, During, and After the two MBES surveys. In both years, the global Moran’s I statistic suggested significant spatial clustering of foraging events on the SOAR during all analysis periods (Before, During, and After). In addition, a Kruskal-Wallis (comparison) test of both years revealed that the number of foraging events across analysis periods were similar within a given year. In 2017, the local Getis-Ord Gi∗ analysis identified hot spots of foraging activity in the same general area of the SOAR during all analysis periods. This local result, in combination with the global and comparison results of 2017, suggest there was no obvious period-related change detected in foraging effort associated with the 2017 MBES survey at the resolution measurable with the hydrophone array. In 2019, the foraging hot spot area shifted from the southernmost corner of the SOAR Before, to the center During, and was split between the two locations After the MBES survey. Due to the pattern of period-related spatial change identified in 2019, and the lack of change detected in 2017, it was unclear whether the change detected in 2019 was a result of MBES activity or some other environmental factor. Nonetheless, the results strongly suggest that the level of detected foraging during either MBES survey did not change, and most of the foraging effort remained in the historically well-utilized foraging locations of Cuvier’s beaked whales on the SOAR.

scholarly journals Hotspots of female genital mutilation/cutting and associated factors among girls in Ethiopia: a spatial and multilevel analysis

2020 ◽  
Author(s):  
Tesfahun Taddege Geremew ◽  
Muluken Azage ◽  
Endalkachew Worku
Keyword(s):  
Female Genital Mutilation ◽  
Associated Factors ◽  
Spatial Clustering ◽  
Household Wealth ◽  
Community Level ◽  
Moran’S I ◽  
Moran's I ◽  
Factors Affecting ◽  
Genital Mutilation ◽  
Female Genital

Abstract Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice that violates the human rights of girls and women. It is widely practiced mainly in Africa including Ethiopia. There are a number of studies on the prevalence of FGM/C in Ethiopia. However, little has been devoted to its spatial epidemiology and associated factors. Hence, this study aimed to explore the spatial pattern and factors affecting FGM/C among girls in Ethiopia. Methods : A further analysis of the 2016 Ethiopia Demographic and Health Survey data was conducted, and a total of 6,985 girls nested in 603 enumeration areas were included. Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Kulldorff’s spatial scan statistics were used to detect spatial clusters of FGM/C. Multilevel logistic regression models were fitted to identify individual and community level factors affecting FGM/C. Results : Spatial clustering of FGM/C was observed (Moran’s I=0.31, p-value < 0.01), and eight significant clusters of FGM/C were detected. The most likely primary cluster was detected in the neighborhood areas of Amhara, Afar, Tigray and Oromia regions (LLR = 279.0, p< 0.01), the secondary cluster in Tigray region (LLR=67.3, p<0.01), and the third cluster in Somali region (LLR=55.5, P<0.01). In About 83% variation in the odds of FGM/C was attributed to both individual and community level factors. At individual level, older maternal age, higher number of living children, maternal circumcision, perceived believes as FGM/C is required by religion, and supporting the continuation of FGM/C practice were factors to increase the odds of FGM/C, whereas, secondary/higher maternal education, better household wealth, and media exposure were factors decreasing the odds of FGM/C. Place of residency, Region and Ethnicity were the community level factors associated with FGM/C. Conclusions: Spatial clustering of FGM/C among girls was observed, and FGM/C hotspots were detected in Afar, Amhara, Tigray, BenishangulGumuz, Oromia, SNNPR and Somali regions including Dire Dawa Town. Both individual and community level factors play a significant role in the practice of FGM/C. Hence, FGM/C hotspots require priority interventions, and it is also better to consider both individual and community level factors.

GIS Analysis of Spatial Clustering and Temporal Change in Weeds of Grass Seed Crops

Weed Science ◽  
2008 ◽  
Vol 56 (5) ◽  
pp. 647-669 ◽  
Author(s):  
George W. Mueller-Warrant ◽  
Gerald W. Whittaker ◽  
William C. Young
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Clustering ◽  
Distribution Patterns ◽  
Italian Ryegrass ◽  
Moran’S I ◽  
Moran's I ◽  
Grass Seed ◽  
Field Bindweed ◽  
Seed Crops ◽  
Inverse Distance

Ten years of Oregon Seed Certification Service (OSCS) preharvest field inspections converted from a nonspatial database to a geographic information system (GIS) were analyzed for patterns in spatial distribution of occurrence and severity of the 36 most common weeds of grass seed crops. This was done under the assumptions that those patterns would be primarily consequences of interactions among farming practices, soil properties, and biological traits of the weeds, and that improved understanding of the interactions would benefit the grass seed industry. Kriging, Ripley's K-function, and both Moran's I spatial autocorrelation and Getis-Ord General G high/low clustering using the multiple fixed distance band option all produced roughly similar classifications of weeds possessing strongest and weakest spatial clustering patterns. When Moran's I and General G analyses of maximum weed severity observed within individual fields over the life of stands were conducted using the inverse distance weighting option, however, results were highly sensitive to the presence of a small number of overlapping fields in the 10-yr record. Addition of any offset in the range from 6 to 6,437 m to measured distances between field centroids in inverse distance weighting matrices removed this sensitivity, and produced results closely matching those for the multiple fixed distance band method. Clustering was significant for maximum severity within fields over the 10-yr period for all 43 weeds and in 78% of single-year analyses. The remaining 22% of single-year cases showed random rather than dispersed distribution patterns. In decreasing order, weeds with strongest inverse-distance spatial autocorrelation were German velvetgrass, field bindweed, roughstalk bluegrass, annual bluegrass, orchardgrass, common velvetgrass, Italian ryegrass,Agrostisspp., and perennial ryegrass. Of these nine weeds, distance for peak spatial autocorrelation ranged from 2 km forAgrostisspp. to 34 km for common velvetgrass. Weeds with stronger spatial autocorrelation had greater range between distance of peak spatial autocorrelation and maximum range of significance. Z-scores for General G high/low clustering were substantially lower than corresponding values for Moran's I spatial autocorrelation, although the same two weeds (German velvetgrass and field bindweed) showed strongest clustering using both measures. Simultaneous patterns in Moran's I and General G implied that management practices relatively ineffective in controlling weeds usually played a greater role in causing weeds to cluster than highly effective practices, although both types of practices impacted Italian ryegrass distribution. Distance of peak high/low clustering among perennial weeds was smallest (1 to 3 km) for Canada thistle, field bindweed,Agrostisspp., and western wildcucumber, likely indicating that these weeds occurred in patchy infestations extending across neighboring fields. Although both wild carrot and field bindweed doubled in average severity over the period from 1994 to 2003, wild carrot was the only weed clearly undergoing an increase in spatial autocorrelation. Soil chemical and physical properties and dummy variables for soil type and crop explained small but significant portions of total variance in redundancy and canonical correspondence analysis of weed occurrence and severity. Fitch-Morgoliash tree diagrams and Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) ordinations revealed substantial differences among soil types in weed occurrence and severity. Gi∗ local hot-spot clustering combined with feature class to raster conversion protected grower expectations of confidentiality while describing dominant spatial features of weed distribution patterns in maps released to the public.

scholarly journals Hotspots of female genital mutilation/cutting and associated factors among girls in Ethiopia: a spatial and multilevel analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tesfahun Taddege Geremew ◽  
Muluken Azage ◽  
Endalkachew Worku Mengesha
Keyword(s):  
Female Genital Mutilation ◽  
Associated Factors ◽  
Spatial Clustering ◽  
Household Wealth ◽  
Community Level ◽  
Moran’S I ◽  
Moran's I ◽  
Factors Affecting ◽  
Genital Mutilation ◽  
Female Genital

Abstract Background Female genital mutilation/cutting (FGM/C) is a harmful traditional practice that violates the human rights of girls and women. It is widely practiced mainly in Africa including Ethiopia. There are a number of studies on the prevalence of FGM/C in Ethiopia. However, little has been devoted to its spatial epidemiology and associated factors. Hence, this study aimed to explore the spatial pattern and factors affecting FGM/C among girls in Ethiopia. Methods A further analysis of the 2016 Ethiopia Demographic and Health Survey data was conducted, and a total of 6985 girls nested in 603 enumeration areas were included in this analysis. Global Moran’s I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Kulldorff’s spatial scan statistics were used to detect spatial clusters of FGM/C. Multilevel logistic regression models were fitted to identify individual and community level factors affecting FGM/C. Results Spatial clustering of FGM/C was observed (Moran’s I = 0.31, p-value < 0.01), and eight significant clusters of FGM/C (hotspots) were detected. The most likely primary SaTScan cluster was detected in the neighborhood areas of Amhara, Afar, Tigray and Oromia regions (LLR = 279.0, p < 0.01), the secondary cluster in Tigray region (LLR = 67.3, p < 0.01), and the third cluster in Somali region (LLR = 55.5, P < 0.01). In the final best fit model, about 83% variation in the odds of FGM/C was attributed to both individual and community level factors. At individual level, older maternal age, higher number of living children, maternal circumcision, perceived beliefs as FGM/C are required by religion, and supporting the continuation of FGM/C practice were factors to increase the odds of FGM/C, whereas, secondary or higher maternal education, better household wealth, and regular media exposure were factors decreasing the odds of FGM/C. Place of residency, Region and Ethnicity were also among the community level factors associated with FGM/C. Conclusions In this study, spatial clustering of FGM/C among girls was observed in Ethiopia, and FGM/C hotspots were detected in Afar, Amhara, Tigray, Benishangul Gumuz, Oromia, SNNPR and Somali regions including Dire Dawa Town. Both individual and community level factors play a significant role in the practice of FGM/C. Hence, FGM/C hotspots require priority interventions, and it is also better if the targeted interventions consider both individual and community level factors.

Urban signatures in the spatial clustering of precipitation extremes over mainland China

2020 ◽  
Author(s):  
Dashan Wang ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dagang Wang ◽  
Zhenzhong Zeng
Keyword(s):  
River Basin ◽  
Pearl River Delta ◽  
Spatial Clustering ◽  
River Delta ◽  
Precipitation Extremes ◽  
Moran’S I ◽  
Pearl River ◽  
Moran's I ◽  
The Pearl River Delta ◽  
The Pearl River

AbstractUrban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation is still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008-2015 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47 and 0.37 for the intensity percentiles of ≥95%, 75-95% and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River Basin, the Yangtze River Basin and the Pearl River Basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I =0.47) and the cold frontal system in the Pearl River Delta (Moran’s I =0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions (e.g., RH >75% over Beijing and RH >85% over the Pearl River Delta) and warmer conditions (T >25°C over Beijing and T >28°C over the Pearl River Delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.

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